Leveraging open-source large language models (LLMs) in scoping reviews: a case study on disability and AI applications.

Abstract

Large language models (LLMs) have the potential to offer solutions for automating many of the manual tasks involved in scientific reviews, including data extraction, literature screening, summarization, and quality assessment. This study aims to evaluate the performance of LLMs in the task of title and abstract screening and full-text data extraction of a scoping review study, by identifying their effectiveness, efficiency, and potential integration into human-based and manual tasks. The following key three steps of a scientific scoping review were automated: 1) Title and Abstract Screening, 2) Full-Text Screening, and 3) Data Extraction based on nine study dimensions. The four most recent lightweight open-source LLMs -Mistral, Vicuna, and Llama 3.2 with 1B and 3B parameters- were applied and evaluated through the steps. Llama 3.2-3B demonstrated the best performance in the title and abstract screening, achieving an accuracy of 66 %, excelling in the exclusion of papers. For full-text screening, it maintained the highest overall accuracy of 65 %, effectively identifying excluded papers. In data extraction, the Mistral model outperformed others across most dimensions, though Llama 3.2-3B excelled in extracting objectives and study implications. The present study underscores both the potential and limitations of LLMs in automating scoping reviews. Automating the entire scoping review without human intervention is sub-optimal. Using a more controlled approach balances the strengths of LLMs with the need for human judgment, supporting not only the replication of scientific reviews but also their continuous refinement and follow-up over time.